






"""simple docstring"""


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				torch.FloatTensor
					lowercase_    :				Optional[torch.FloatTensor]      =  None


def        UpperCAmelCase      (					a__     ,      a__=0.999     ,      a__="cosine"     ,      ):





						'''simple docstring'''






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(a__ ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(a__ ):
																		return math.exp(t * -12.0 )

						else:
												raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )

						lowerCAmelCase   :Union[str, Any]						=			[]
						for i in range(a__ ):
												lowerCAmelCase   :List[str]						=			i / num_diffusion_timesteps
												lowerCAmelCase   :Optional[int]						=			(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(a__ ) / alpha_bar_fn(a__ )     ,      a__ ) )
						return torch.tensor(a__     ,      dtype=torch.floataa )







class 						__UpperCamelCase     (     UpperCamelCase   ,     UpperCamelCase						):
					lowercase_    :				Union[str, Any]      =  1
					@register_to_config
					def __init__(							self							:	Optional[Any]     , UpperCAmelCase							:	int = 1000     , UpperCAmelCase							:	float = 0.0_0_0_1     , UpperCAmelCase							:	float = 0.0_2     , UpperCAmelCase							:	str = "linear"     , UpperCAmelCase							:	Optional[Union[np.ndarray, List[float]]] = None     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	int = 0     , UpperCAmelCase							:	str = "epsilon"     , UpperCAmelCase							:	float = 1.0     , **UpperCAmelCase							:	Optional[int]     , )	->       Dict:
											if kwargs.get('set_alpha_to_one'     , UpperCAmelCase   ) is not None:
																	lowerCAmelCase   :Optional[Any]						=			(
																	    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
																	)
																	deprecate('set_alpha_to_one'     , '1.0.0'     , UpperCAmelCase     , standard_warn=UpperCAmelCase   )
																	lowerCAmelCase   :Dict						=			kwargs['set_alpha_to_one']
											if trained_betas is not None:
																	lowerCAmelCase   :Any						=			torch.tensor(UpperCAmelCase     , dtype=torch.floataa   )
											elif beta_schedule == "linear":
																	lowerCAmelCase   :Union[str, Any]						=			torch.linspace(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , dtype=torch.floataa   )
											elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	lowerCAmelCase   :Union[str, Any]						=			(
																	    torch.linspace(beta_start**0.5     , beta_end**0.5     , UpperCAmelCase     , dtype=torch.floataa   ) ** 2
																	)
											elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	lowerCAmelCase   :Optional[Any]						=			betas_for_alpha_bar(UpperCAmelCase   )
											else:
																	raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""   )

											lowerCAmelCase   :Tuple						=			1.0 - self.betas
											lowerCAmelCase   :Optional[Any]						=			torch.cumprod(self.alphas     , dim=0   )

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											lowerCAmelCase   :str						=			torch.tensor(0.0   ) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											lowerCAmelCase   :List[Any]						=			1.0

											# setable values
											lowerCAmelCase   :List[str]						=			None
											lowerCAmelCase   :int						=			torch.from_numpy(np.arange(0     , UpperCAmelCase   ).copy().astype(np.intaa   )   )
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	torch.FloatTensor     , UpperCAmelCase							:	Optional[int] = None   )	->       torch.FloatTensor:
											return sample
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	int     , UpperCAmelCase							:	Union[str, torch.device] = None   )	->       int:

											if num_inference_steps > self.config.num_train_timesteps:
																	raise ValueError(
																	    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																	    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																	    f""" maximal {self.config.num_train_timesteps} timesteps."""   )

											lowerCAmelCase   :Optional[int]						=			num_inference_steps
											lowerCAmelCase   :Tuple						=			self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											lowerCAmelCase   :List[str]						=			(np.arange(0     , UpperCAmelCase   ) * step_ratio).round().copy().astype(np.intaa   )
											lowerCAmelCase   :str						=			torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
											self.timesteps += self.config.steps_offset
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	torch.FloatTensor     , UpperCAmelCase							:	int     , UpperCAmelCase							:	torch.FloatTensor     , UpperCAmelCase							:	float = 0.0     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	Optional[torch.FloatTensor] = None     , UpperCAmelCase							:	bool = True     , )	->       Union[DDIMSchedulerOutput, Tuple]:
											# 1. get previous step value (=t+1)
											lowerCAmelCase   :Tuple						=			timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											lowerCAmelCase   :Dict						=			self.alphas_cumprod[timestep]
											lowerCAmelCase   :Dict						=			(
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											lowerCAmelCase   :Optional[int]						=			1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																	lowerCAmelCase   :Union[str, Any]						=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																	lowerCAmelCase   :Any						=			model_output
											elif self.config.prediction_type == "sample":
																	lowerCAmelCase   :Optional[int]						=			model_output
																	lowerCAmelCase   :int						=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																	lowerCAmelCase   :str						=			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																	lowerCAmelCase   :Dict						=			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																	raise ValueError(
																	    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																	    ' `v_prediction`'   )

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																	lowerCAmelCase   :Optional[Any]						=			pred_original_sample.clamp(
																	    -self.config.clip_sample_range     , self.config.clip_sample_range   )

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											lowerCAmelCase   :Union[str, Any]						=			(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											lowerCAmelCase   :List[str]						=			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																	return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=UpperCAmelCase     , pred_original_sample=UpperCAmelCase   )
					def __len__(							self							:	List[str]   )	->       Union[str, Any]:
											return self.config.num_train_timesteps







"""simple docstring"""


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		import torch

		from transformers.modeling_outputs import BaseModelOutput

		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	Union[str, Any]     , **UpperCAmelCase							:	List[Any]   )	->       int:
											super().__init__(**UpperCAmelCase   )

											if self.framework == "tf":
																	raise ValueError(f"""The {self.__class__} is only available in PyTorch."""   )

											requires_backends(self     , 'vision'   )
											self.check_model_type(UpperCAmelCase   )
					def __call__(							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, "Image.Image", List[Dict[str, Any]]]     , UpperCAmelCase							:	Union[str, List[str]] = None     , **UpperCAmelCase							:	Dict     , )	->       Any:
											if "text_queries" in kwargs:
																	lowerCAmelCase   :Dict						=			kwargs.pop('text_queries'   )

											if isinstance(UpperCAmelCase     , (str, Image.Image)   ):
																	lowerCAmelCase   :List[str]						=			{'image': image, 'candidate_labels': candidate_labels}
											else:
																	lowerCAmelCase   :Union[str, Any]						=			image
											lowerCAmelCase   :Optional[int]						=			super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
											return results
					def        UpperCAmelCase__ (							self							:	List[Any]     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											lowerCAmelCase   :Any						=			{}
											if "threshold" in kwargs:
																	lowerCAmelCase   :int						=			kwargs['threshold']
											if "top_k" in kwargs:
																	lowerCAmelCase   :str						=			kwargs['top_k']
											return {}, {}, postprocess_params
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	Union[str, Any]   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(inputs['image']   )
											lowerCAmelCase   :Dict						=			inputs['candidate_labels']
											if isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																	lowerCAmelCase   :str						=			candidate_labels.split(','   )

											lowerCAmelCase   :str						=			torch.tensor([[image.height, image.width]]     , dtype=torch.intaa   )
											for i, candidate_label in enumerate(UpperCAmelCase   ):
																	lowerCAmelCase   :Any						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																	lowerCAmelCase   :Optional[Any]						=			self.image_processor(UpperCAmelCase     , return_tensors=self.framework   )
																	yield {
																	    "is_last": i == len(UpperCAmelCase   ) - 1,
																	    "target_size": target_size,
																	    "candidate_label": candidate_label,
																	    **text_inputs,
																	    **image_features,
																	}
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       List[Any]:
											lowerCAmelCase   :Union[str, Any]						=			model_inputs.pop('target_size'   )
											lowerCAmelCase   :List[str]						=			model_inputs.pop('candidate_label'   )
											lowerCAmelCase   :Union[str, Any]						=			model_inputs.pop('is_last'   )

											lowerCAmelCase   :int						=			self.model(**UpperCAmelCase   )

											lowerCAmelCase   :Any						=			{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	int=0.1     , UpperCAmelCase							:	Optional[Any]=None   )	->       str:
											lowerCAmelCase   :int						=			[]
											for model_output in model_outputs:
																	lowerCAmelCase   :int						=			model_output['candidate_label']
																	lowerCAmelCase   :List[Any]						=			BaseModelOutput(UpperCAmelCase   )
																	lowerCAmelCase   :Dict						=			self.image_processor.post_process_object_detection(
																	    outputs=UpperCAmelCase     , threshold=UpperCAmelCase     , target_sizes=model_output['target_size']   )[0]

																	for index in outputs["scores"].nonzero():
																							lowerCAmelCase   :Tuple						=			outputs['scores'][index].item()
																							lowerCAmelCase   :int						=			self._get_bounding_box(outputs['boxes'][index][0]   )

																							lowerCAmelCase   :Optional[int]						=			{'score': score, 'label': label, 'box': box}
																							results.append(UpperCAmelCase   )

											lowerCAmelCase   :Tuple						=			sorted(UpperCAmelCase     , key=lambda UpperCAmelCase   : x["score"]     , reverse=UpperCAmelCase   )
											if top_k:
																	lowerCAmelCase   :Dict						=			results[:top_k]

											return results
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	"torch.Tensor"   )	->       Dict[str, int]:
											if self.framework != "pt":
																	raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'   )
											lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			box.int().tolist()
											lowerCAmelCase   :Union[str, Any]						=			{
											    'xmin': xmin,
											    'ymin': ymin,
											    'xmax': xmax,
											    'ymax': ymax,
											}
											return bbox